Learning "best" kernels from data in Gaussian process regression. With application to aerodynamics. (arXiv:2206.02563v2 [stat.ML] UPDATED)

This paper introduces algorithms to select/design kernels in Gaussian process
regression/kriging surrogate modeling techniques. We adopt the setting of
kernel method solutions in ad hoc functional spaces, namely Reproducing Kernel
Hilbert Spaces (RKHS), to solve the problem of approximating a regular target
function given observations of it, i.e. supervised learning. A first class of
algorithms is kernel flow, which was introduced in the context of
classification in machine learning. It can be seen as a cross-validation
procedure whereby a "best" …

aerodynamics application arxiv data learning process regression